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Denoisers are algorithms that, given a noisy image from a ray tracing engine, produce a clean image.<ref>https://developer.nvidia.com/blog/ray-tracing-essentials-part-7-denoising-for-ray-tracing/</ref> This can be done through multiple means, such as blurring neighboring pixels to smooth out images. | Denoisers are algorithms that, given a noisy image from a [[Render Engine#Ray Tracing|ray tracing engine]], produce a clean image.<ref>https://developer.nvidia.com/blog/ray-tracing-essentials-part-7-denoising-for-ray-tracing/</ref> This can be done through multiple means, such as blurring neighboring pixels to smooth out images. | ||
Many ray tracing engines utilize a form of [[Monte Carlo Integration]] in order to accurate approximate a scene. As a consequence however, noise is created. While increasing the number of samples can reduce the noise and eventually give a clean image, the processing time is impractical for most use cases, such as rendering an animation. Denoisers allow for reducing the amount of sampling done when rendering, while keeping a clean result. | Many ray tracing engines utilize a form of [[Monte Carlo Integration]] in order to accurate approximate a scene. As a consequence however, noise is created. While increasing the number of samples can reduce the noise and eventually give a clean image, the processing time is impractical for most use cases, such as rendering an animation. Denoisers allow for reducing the amount of sampling done when rendering, while keeping a clean result. | ||
Revision as of 04:37, 6 July 2025
Denoisers are algorithms that, given a noisy image from a ray tracing engine, produce a clean image.[1] This can be done through multiple means, such as blurring neighboring pixels to smooth out images.
Many ray tracing engines utilize a form of Monte Carlo Integration in order to accurate approximate a scene. As a consequence however, noise is created. While increasing the number of samples can reduce the noise and eventually give a clean image, the processing time is impractical for most use cases, such as rendering an animation. Denoisers allow for reducing the amount of sampling done when rendering, while keeping a clean result.
Many denoisers, such as Intel's OIDN and Nvidia's OptiX, utilize machine learning to more accurately blur noise in portions of an image without losing too much detail from a uniform blur.[2][3][4]
One common issue with denoisers, namely non-temporal denoisers, is that they can cause flickering issues in animation, due to artifacts that aren't stable across multiple frames.